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Physics-informed machine learning

Machine learning used to represent physics-based and/or engineering models

Papers

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Showing 61–70 of 192 papers

TitleStatusHype
Kolmogorov n-Widths for Multitask Physics-Informed Machine Learning (PIML) Methods: Towards Robust MetricsCode0
Neural oscillators for generalization of physics-informed machine learningCode0
From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learning—0
Fourier-Invertible Neural Encoder (FINE) for Homogeneous Flows—0
FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design—0
A Physics-informed machine learning model for time-dependent wave runup prediction—0
A Mechanism-Learning Deeply Coupled Model for Remote Sensing Retrieval of Global Land Surface Temperature—0
Filtered Partial Differential Equations: a robust surrogate constraint in physics-informed deep learning framework—0
A physics-informed machine learning model for reconstruction of dynamic loads—0
Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations—0
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